{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score,recall_score\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "62    52    1   3       118   186    0        0      190      0      0.0   \n",
       "5     57    1   0       140   192    0        1      148      0      0.4   \n",
       "152   64    1   3       170   227    0        0      155      0      0.6   \n",
       "94    45    0   1       112   160    0        1      138      0      0.0   \n",
       "107   45    0   0       138   236    0        0      152      1      0.2   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "62       1   0     1       1  \n",
       "5        1   0     1       1  \n",
       "152      1   0     3       1  \n",
       "94       1   0     2       1  \n",
       "107      1   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart.csv\")\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>303.0</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>303.0</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>274.5</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>153.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>303.0</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.544554</td>\n",
       "      <td>0.498835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min    25%    50%    75%    max\n",
       "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
       "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
       "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
       "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
       "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
       "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
       "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
       "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
       "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
       "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
       "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    143\n",
       "2     87\n",
       "1     50\n",
       "3     23\n",
       "Name: cp, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('sex', hue = 'target',data = df)\n",
    "\n",
    "plt.title('Heart Disease Frequency for Gender')\n",
    "plt.legend(['No Disease','Yes Disease'])\n",
    "\n",
    "plt.xlabel('Gender (0 = Female, 1 = Male)')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bb862226bpPdJbCv97Gc/yw477JDDDz88Bx10UC655JLsu+++Oeuss3Ldddcl6V3+dvXVV6/DWQAANjYCIwCAtXjb296WX//616sen3jiifnUpz6VefPm5bOf/Ww+9rGPjfu8M888M7vttlse97jH5YMf/GDOPvvse80wuvXWW3PggQdm3rx5efrTn77qJtsnnnhiLrroosybNy9z587N4sWLkyTveMc78s53vjNPfepTOzewPvPMM7PLLrtkt912yxVXXJGFCxdm7ty5+cAHPpD99tsv8+bNy7Of/exce+21k316AIANUI13Lf2k7Ljqk0kOTHJda22X/rLjkzwvyZ1J/iPJa1prN61tX/Pnz28XXXTRUPoEAKafZcuW3StYYficdwDYuFTVktba/PHWDXOG0aeTPGe1Zd9IsktrbV6Sf0vyziHWBwAAAGAdDC0waq19N8mNqy37emvtrv7D7yeZPaz6AAAAAKybUd7D6LVJ/mmE9QEAAAAYx4xRFK2qv0hyV5LT17DNoiSLkmTOnDlT1BkAABuCPY86deBtlxy/cIidAMD6acpnGFXVIendDPuVbQ133G6tndxam99amz9r1qypaxAAAABgIzelM4yq6jlJ/jzJ01tr/zOVtQEAAAAYzNBmGFXVPyS5IMlOVbWiqv40yUlJtkjyjapaWlWLh1UfAGBdtday995755/+6Xe3W/zc5z6X5zxn9Q+AHcx2222XXXfdNbvuumvmzp2bd7/73fnNb36TJLnmmmvyJ3/yJ5PSNwDAZBnaDKPW2svHWfyJYdUDADZcy4/ddVL3N+eYS9e4vqqyePHivPjFL84+++yTu+++O3/xF3+Rf/7nf17nmt/61rey9dZb57bbbsuiRYuyaNGifOYzn8mjH/3onHXWWeu8XwCAYRjlp6QBAExbu+yyS573vOflwx/+cN7//vfnVa96VY477rgsWLAgu+++e770pS8lSS677LLstdde2W233TJv3rxceeWVa9zvQx7ykCxevDhf/OIXc+ONN+aqq67KLrvsssZ9nXbaaauWv/71r8/dd9+dJHnDG96Q+fPn5wlPeELe+973rqpx9NFHZ+7cuZk3b17e/va3J0muv/76vOhFL8qCBQuyYMGCnH/++ZN+zgCADcdIPiUNAGB98N73vjd77LFHHvCAB+TAAw/MM5/5zHzyk5/MTTfdlL322ivPetazsnjx4hxxxBF55StfmTvvvHNVmLMmW265ZbbffvtceeWVeeQjH7lq+Xj7WrZsWc4888ycf/752WyzzfLGN74xp59+ehYuXJjjjjsuW221Ve6+++7su+++ueSSSzJ79uycc845ueKKK1JVuemmm5IkRxxxRN761rdm7733zvLly7P//vtn2bJlQzt3AMD6TWAEADCBBz/4wXnpS1+ahzzkIfnc5z6XL3/5yznhhBOSJHfccUeWL1+eJz/5yTnuuOOyYsWKHHzwwdlxxx0H2vd4HxY73r7OO++8LFmyJAsWLEiS3H777dlmm22S9O6rdPLJJ+euu+7Ktddem8svvzxz587NzJkzc9hhh+W5z31uDjzwwCTJueeem8svv3xVrVtuuSW33nprtthii/t1jgCADZPACABgDTbZZJNssskmaa3l7LPPzk477dRZv/POO+eJT3xivvrVr2b//ffPKaeckmc+85lr3Oett96aq666Ko973ONy8803r1r+ile84l77aq3lkEMOyYc+9KHOPn7+85/nhBNOyA9/+MM8/OEPz6GHHpo77rgjM2bMyIUXXpjzzjsvZ5xxRk466aR885vfzD333JMLLrggm2+++eSdHABgg+UeRgAAA9h///3zN3/zN6tmBl188cVJkp/97GfZYYcdcvjhh+eggw7KJZdcssb93HbbbXnjG9+YF7zgBXn4wx/eWTfevvbdd9+cddZZue6665IkN954Y66++urccsstefCDH5yHPvSh+dWvfrXqE91uu+223HzzzTnggAPy0Y9+NEuXLk2S7LfffjnppJNW1Vq5HABgPGYYAQAM4D3veU+OPPLIzJs3L621bLfddvnKV76SM888M6eddlo222yzPOpRj8oxxxwz7vP32WeftNZyzz335IUvfGHe85733Gub8fa11VZb5QMf+ED222+/3HPPPdlss83yt3/7t3nSk56U3XffPU94whOyww475KlPfWqS3uyl5z//+bnjjjvSWstHPvKRJMmJJ56YN73pTZk3b17uuuuuPO1pT8vixYuHd8IAgPVajXf9/HQzf/78dtFFF426DQBgiixbtiw777zzqNvY6GxI533Po04deNslxy8cYicAMH1V1ZLW2vzx1rkkDQAAAIAOgREAAAAAHQIjAAAAADoERgDAtLQ+3GdxQ+J8AwBjCYwAgGln5syZueGGG4QYU6S1lhtuuCEzZ84cdSsAwDQxY9QNAACsbvbs2VmxYkWuv/76Ubey0Zg5c2Zmz5496jYAgGlCYAQATDubbbZZtt9++1G3AQCw0XJJGgAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBjaIFRVX2yqq6rqp+MWbZVVX2jqq7s//vwYdUHAAAAYN0Mc4bRp5M8Z7VlRyc5r7W2Y5Lz+o8BAAAAmEaGFhi11r6b5MbVFj8/yWf6338myQuGVR8AAACAdTPV9zB6ZGvt2iTp/7vNFNcHAAAAYC1mjLqBiVTVoiSLkmTOnDkj7gYAYDDLj9114G3nHHPpEDuZfvY86tSBt11y/MIhdgIArM1UzzD6VVX9XpL0/71uog1baye31ua31ubPmjVryhoEAAAA2NhNdWD0j0kO6X9/SJIvTXF9AAAAANZiaIFRVf1DkguS7FRVK6rqT5P8ZZJnV9WVSZ7dfwwAAADANDK0exi11l4+wap9h1UTAAAAgPtvqi9JAwAAAGCaExgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgI4Zo24AmFzLj9114G3nHHPpEDsB1jdTPX4MWs9YxXS351GnDrztkuMXDrETAJg8ZhgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOkYSGFXVW6vqsqr6SVX9Q1XNHEUfAAAAANzblAdGVbVtksOTzG+t7ZJk0yQvm+o+AAAAABjfqC5Jm5Fk86qakeRBSa4ZUR8AAAAArGbKA6PW2i+TnJBkeZJrk9zcWvv6VPcBAAAAwPhmTHXBqnp4kucn2T7JTUk+X1Wvaq2dttp2i5IsSpI5c+ZMdZvARm75sbsOvO2cYy4dYicA8Dt7HnXqwNsuOX7hEDsBYEM3ikvSnpXk562161trv03yhSRPWX2j1trJrbX5rbX5s2bNmvImAQAAADZWowiMlid5UlU9qKoqyb5Jlo2gDwAAAADGMYp7GP0gyVlJfpTk0n4PJ091HwAAAACMb8rvYZQkrbX3JnnvKGoDAAAAsGajuCQNAAAAgGlMYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOmYMslFV7dJa+8mwmwHWL8uP3XXgbeccc+kQOwFgQzPo75jJ+P3i99nE9jzq1IG2W3L8wimrNVn1AFizQWcYLa6qC6vqjVX1sKF2BAAAAMBIDRQYtdb2TvLKJI9JclFV/X1VPXuonQEAAAAwEgPfw6i1dmWSdyf58yRPT3JiVV1RVQcPqzkAAAAApt5AgVFVzauqjyRZluSZSZ7XWtu5//1HhtgfAAAAAFNsoJteJzkpyceTvKu1dvvKha21a6rq3UPpDAAAAICRGDQwOiDJ7a21u5OkqjZJMrO19j+ttc8OrTsAAAAAptyg9zA6N8nmYx4/qL8MAAAAgA3MoIHRzNbabSsf9L9/0HBaAgAAAGCUBg2M/ruq9lj5oKr2THL7GrYHAAAAYD016D2Mjkzy+aq6pv/495K8dDgtAQAAADBKAwVGrbUfVtXjk+yUpJJc0Vr77VA7AwAAAGAkBp1hlCQLkmzXf87uVZXW2qlD6QoAAACAkRkoMKqqzyb5/SRLk9zdX9ySCIwAAAAANjCDzjCan2Rua60NsxkAAAAARm/QT0n7SZJHDbMRAAAAAKaHQWcYbZ3k8qq6MMlvVi5srR00lK4AAAAAGJlBA6P3DbMJAAAAAKaPgQKj1tp3quqxSXZsrZ1bVQ9KsulwWwMAAABgFAa6h1FVvS7JWUn+rr9o2yRfHFZTAAAAAIzOoDe9flOSpya5JUlaa1cm2WZYTQEAAAAwOoMGRr9prd258kFVzUjShtMSAAAAAKM0aGD0nap6V5LNq+rZST6f5MvDawsAAACAURk0MDo6yfVJLk3y+iRfS/LuYTUFAAAAwOgM+ilp9yT5eP8LAAAAgA3YQIFRVf0849yzqLW2w6R3BAAAAMBIDRQYJZk/5vuZSV6cZKvJbwcAAACAURvoHkattRvGfP2ytfbRJM8ccm8AAAAAjMCgl6TtMebhJunNONpiKB0BAAAAMFKDXpL2V2O+vyvJVUleMundAAAAADByg35K2j7DbgQAAACA6WHQS9L+15rWt9b++r4UraqHJTklyS7pffraa1trF9yXfQAAAAAwHPflU9IWJPnH/uPnJflukl+sY92PJfnn1tqfVNUDkjxoHfcDAAAAwCQbNDDaOskerbVbk6Sq3pfk8621w+5rwaraMsnTkhyaJK21O5PceV/3AwAAAMBwbDLgdnPSDXXuTLLdOtbcIcn1ST5VVRdX1SlV9eB13BcAAAAAk2zQGUafTXJhVZ2T3j2HXpjk1PtRc48kb2mt/aCqPpbk6CTvGbtRVS1KsihJ5syZs46lAKa/5cfuOvC2c465dIidwIbr/v6c7XnU4H/2nLPFwJtOC4Me25LjFw65E5g69+Vneirf+/4mmNh0fc1gQzbQDKPW2nFJXpPkv5LclOQ1rbUPrmPNFUlWtNZ+0H98VnoB0uo1T26tzW+tzZ81a9Y6lgIAAADgvhr0krSkd2PqW1prH0uyoqq2X5eCrbX/TPKLqtqpv2jfJJevy74AAAAAmHwDXZJWVVFvjBgAABeDSURBVO9N75PSdkryqSSbJTktyVPXse5bkpze/4S0n6U3ewkAAACAaWDQexi9MMnuSX6UJK21a6pqna/Qb60tTS+AAgAAAGCaGfSStDtbay29G17Hp5oBAAAAbLgGDYw+V1V/l+RhVfW6JOcm+fjw2gIAAABgVNZ6SVpVVZIzkzw+yS3p3cfomNbaN4bcGwAAAAAjsNbAqLXWquqLrbU9kwiJAAAAADZwg16S9v2qWjDUTgAAAACYFgb9lLR9kvxZVV2V5L+TVHqTj+YNqzEAAAAARmONgVFVzWmtLU/yx1PUDwAAAAAjtrYZRl9Mskdr7eqqOru19qKpaAoAAACA0VnbPYxqzPc7DLMRAAAAAKaHtQVGbYLvAQAAANhAre2StD+sqlvSm2m0ef/75Hc3vd5yqN0BAAAAMOXWGBi11jadqkYAAAAAmB7WdkkaAAAAABsZgREAAAAAHQIjAAAAADoERgAAAAB0CIwAAAAA6BAYAQAAANAhMAIAAACgQ2AEAAAAQIfACAAAAIAOgREAAAAAHQIjAAAAADoERgAAAAB0CIwAAAAA6BAYAQAAANAhMAIAAACgQ2AEAAAAQIfACAAAAICOGaNuAGBQy4/ddaDt5hxz6ZA7ARiePY86deBtlxy/cIiddA06BifG4TVxHgFYX5hhBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgYWWBUVZtW1cVV9ZVR9QAAAADAvY1yhtERSZaNsD4AAAAA4xhJYFRVs5M8N8kpo6gPAAAAwMRGNcPoo0nekeSeEdUHAAAAYAIzprpgVR2Y5LrW2pKqesYatluUZFGSzJkzZ4q6A9iwLT9214G3nXPMpUPsZPJtqMe2vh3XnkedOvC252wxxEaADdr6NDZO5bh432odP/C2k3EOB33NRv16Ab8zihlGT01yUFVdleSMJM+sqtNW36i1dnJrbX5rbf6sWbOmukcAAACAjdaUB0attXe21ma31rZL8rIk32ytvWqq+wAAAABgfKP8lDQAAAAApqEpv4fRWK21byf59ih7AAAAAKDLDCMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBjxqgbgFFZfuyuA28755hLh9jJxm3Po04deNtztlh/ak2l6Xpck/EztqEem/c9k8XvMtbE+2NyOI8Tm66/Y7xmExv0NVty/MIhd9LlNZuezDACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdEx5YFRVj6mqb1XVsqq6rKqOmOoeAAAAAJjYjBHUvCvJ21prP6qqLZIsqapvtNYuH0EvAAAAAKxmymcYtdauba39qP/9rUmWJdl2qvsAAAAAYHwjvYdRVW2XZPckPxhlHwAAAAD8ziguSUuSVNVDkpyd5MjW2i3jrF+UZFGSzJkzZ4q7u++WH7vrwNvOOebSey3b86hTB37+OVscf79q3Rf397ima63JcF9esyXHLxxiJ0xH9+1neoiNDMGgx7ahHley/h3bhsprBsAw+L8Z9IxkhlFVbZZeWHR6a+0L423TWju5tTa/tTZ/1qxZU9sgAAAAwEZsFJ+SVkk+kWRZa+2vp7o+AAAAAGs2ihlGT03y6iTPrKql/a8DRtAHAAAAAOOY8nsYtdb+NUlNdV0AAAAABjPST0kDAAAAYPoRGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACADoERAAAAAB0CIwAAAAA6BEYAAAAAdAiMAAAAAOgQGAEAAADQITACAAAAoENgBAAAAECHwAgAAACAjhmjbmCYlh+760DbzTnm0iF3snHb86hTB9runC2mrtZk1RvUoO/FZPz341Qe13Q9hwDA9HV//9aBjdV0/dt7Q/6Znspjm8r/By45fuHA2w7KDCMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAh8AIAAAAgA6BEQAAAAAdAiMAAAAAOgRGAAAAAHQIjAAAAADoEBgBAAAA0CEwAgAAAKBDYAQAAABAx0gCo6p6TlX9tKr+vaqOHkUPAAAAAIxvygOjqto0yd8m+eMkc5O8vKrmTnUfAAAAAIxvFDOM9kry7621n7XW7kxyRpLnj6APAAAAAMYxisBo2yS/GPN4RX8ZAAAAANNAtdamtmDVi5Ps31o7rP/41Un2aq29ZbXtFiVZ1H+4U5KfrkO5rZP8+n60q9aGW2uq66m1/tVTa/2qNdX11Fr/6qm1ftWa6npqrV+1prqeWutfPbXWr1pTXU+trse21maNt2LG/etnnaxI8pgxj2cnuWb1jVprJyc5+f4UqqqLWmvz788+1Nowa011PbXWv3pqrV+1prqeWutfPbXWr1pTXU+t9avWVNdTa/2rp9b6VWuq66k1uFFckvbDJDtW1fZV9YAkL0vyjyPoAwAAAIBxTPkMo9baXVX15iT/kmTTJJ9srV021X0AAAAAML5RXJKW1trXknxtCkrdr0va1Nqga011PbXWv3pqrV+1prqeWutfPbXWr1pTXU+t9avWVNdTa/2rp9b6VWuq66k1oCm/6TUAAAAA09so7mEEAAAAwDS2QQRGVfWYqvpWVS2rqsuq6oj+8j+sqguq6tKq+nJVbTkJtWZW1YVV9eN+rfevtv7tVdWqauth1aqq91XVL6tqaf/rgPtbay31zhxT66qqWjrEWrtV1ff7tS6qqr2GWGvS3x9jam5aVRdX1Vf6j7eqqm9U1ZX9fx8+xFov7h/nPVU1qXfJH6fW8VV1RVVdUlXnVNXDhlzvf/drLa2qr1fVo4dVa8zySfuZnqjWsH6mx6vVX/aWqvpp/33yf4ZVaxhjx1rqTfr4sYZawxw/rurvd2lVXdRfNpQxZIJaQxlDJqg1lDFkglpDGT/GqzVm3TDGj/GObVh/F4x7bMMYQyY4rqGMIRPUGsr4MUGtYY4fD6uqs/o/V8uq6slDHD/GqzWs8WO8WsMaP8arNcy/P+5Vb8y6SR1DJji2YY0f4x7XkMaP8Y5rWOPHeLWG+ffHePWG8X/cncacr6VVdUtVHTmM8WMNtSZ9/Jio1pj1k/0zNmG9yX7vr+E8Tu57v7W23n8l+b0ke/S/3yLJvyWZm94nsj29v/y1Sf73JNSqJA/pf79Zkh8keVL/8WPSu5n31Um2HlatJO9L8vYhnMcJj23MNn+V5JghHtvXk/xxf/kBSb49xFqT/v4YU/N/Jfn7JF/pP/4/SY7uf390kg8PsdbOSXZK8u0k8yf5PbJ6rf2SzOh//+HJPK4J6m05Zt3hSRYPq1Z/2aT+TK/huIbyMz1BrX2SnJvkgf3H2wzzHI5ZNyljx1qObdLHjzXUGub4cdXq77dhjSET1BrKGDJBraGMIRPUGsr4MV6t/vJhjR/jHdtQxpAJag1lDJnoPI5ZP2ljyATHNZTxY4Jawxw/PpPksP73D0jysCGOH+PVGtb4MV6tYY0f49Ua5t8f96rX/37Sx5AJjm1Y48d4tYY1fox7Dsesn8zxY7zjGubfH+PVG9oY0t/npkn+M8ljhzV+TFBraP+HWb1W//FQfk9PcGxD+/t7vGMbs/x+v/c3iBlGrbVrW2s/6n9/a5JlSbZN7w333f5m30jyokmo1Vprt/Ufbtb/WnkjqI8keceYx8OsNenWVq+qKslLkvzDEGu1JCtT8ocmuWaItSb9/ZEkVTU7yXOTnDJm8fPTG/DT//cFw6rVWlvWWvvpZOx/gFpfb63d1X/4/SSzh1zvljGbPDiT9PMwwWuWTPLP9FpqTboJar0hyV+21n6TJK2164ZYa+W6SRs71lJv0sePNdQayvixBkMZQ8YzrDFkglpDG0PGqTWU8WMNJn38mCaGMoasyTDGkHEMZfyYwLD+/tgyydOSfCJJWmt3ttZuyhDGj4lqDWP8WEOtSR8/1lBrWH9/TPSaJZM8hqyl1qRaQ61JHz/WdlyTOX6sodaw/v6YqN6w/wbZN8l/tNauzvD//lhVawr+/hh7XMnwf0+PrTfs352rH9ukvfc3iMBorKraLsnu6c0i+UmSg/qrXpxeijgZNTbtT+26Lsk3Wms/qKqDkvyytfbjyaixplr9VW+u3tTYT07G1MAB6iXJHyX5VWvtyiHWOjLJ8VX1iyQnJHnnEGsN5f2R5KPpDT73jFn2yNbatUkv4EyyzRBrDcvaar02yT8Nu15VHdd/f7wyyTHDqjWsn+nxavUN42d6vFqPS/JHVfWDqvpOVS0YYq2VJnXsWEO9oYwfE9Qa1viR9P5w+XpVLan6f+3df6jddR3H8edbr0a6oIipC8VRqEEz1oTARiVmVBDScsEGlZT9xKKMImpFQkTWH1EZWWCTfixCWWrMmEm/IKgcK8NZLvoxauQqiQyVEPPdH9/vtnu37/d777bP+95tPB8wtnPu2Xl9v+d8vq/zuZ97vufGO/rrqjpkKKvKfFktO2Qwq6g/Dssq7I/BvF5FhwxlVXXI1Pho3SFDWVX9MZRV1R/PBf4J3BLdKbQ3R8SZ1PTHWFaFhWS16o/RrKL+GMwr6pCpx7F1f4xlVfTHfOOjZX+MZVX1x1he5RwEYAMHFxmq5h9DWdUOZBW/Th+WR91r51DWfk3G/km1YBQRy4CtwPv7nwS8Fbg2InbSnar2RIuczPxfZq6m+0nGiyPihcAm2r14TGWtAm4CngesBh6ie6tZZd5+G2l4QI9kvRu4LjPPA66jX1Evymo+PiLitcA/MnPnsd7XiZQVEZuAJ4Et1XmZuakfH1uA91RkRcQZFBzTE/vV/JieyJoBnkV3WuaHgFv7n0BUZO3XtDsm8pr3x0RWyetLb21mrgFe02e8rOF9H5dZrTtkLKt1f0xklcwJJvKq5gVDWc07ZCJrv6YdMpJVMv8YyarqjxlgDXBTZr4IeIzuFJIKx01W4/4YzSrqj6G866npkLF9q+iPsayK/phvLLbsj7Gsqv4Yyyubg0TE6XSLUbe1us/jLatqnj+W119V9do59Ti2GfvZ+Fy9pfpDd5rR3cAHRr5+IXBvQe4ngI/TvXNlT//nSeAvwDkFWR885LqVwK6ix/RAHt0g/ztwbmUW8AgQ/XUB/Kdyv1qPD+DTwN5+HOwDHge+BewGVvS3WQHsrsqa9fWf0Oj836ks4Grg58AZDZ+jyX3rb3N+i7E/krW14phe4H41OaYnxuJ24LJZt/sjsLxwfDTvjol9a94fC3zOSl5f+vu+nq4bm3fIWNasy806ZCqrokOm9qu/rkl/jGQtypxgYt+adMhYVkWHzDM+qucf+/drMeYfQ89Xs/4AzgH2zLr8UuCuiv4Yy5p1uVl/TGW17o/59qu/rll/jOT9sKJDFrhvTfpjYixWzEGmxkfT/pjYr5L+WOBz1nQOQncK2g9mXS6bfxyaNev6Zv0xlAVcXHGMzfM4lr12Dj2OLcf+SfEOo3517mvA7zLzc7OuP6v/+xTgY8BXGmQtj/63METE04ErgF9n5lmZuTIzV9J9c7EmM/cVZD0YEStm3Wwd3dsSj9lYXv/lK4AHM3NvcdbfgJf3N7scOOa3j048js3HR2Z+JDPP7cfBBuBHmflG4Ht0kxr6v+8szGpuLCsiXg18GLgyMx9fhLwLZt3sSg6Oz9ZZV1Uc0xP71fyYnhgfd9AdW0TEhXQfoPhwURY07o558pr3x8Rz1rw/+vs7MyKesf/fdB/quouCDpnIam4sq6JDJrKa98dI1o6K/pjI21XRIRPjo3mHzDMWW88/xrIq5h9jz1dJf/Rj7K8RcVF/1SuA31IzBxnLam4sq6I/JrKa98dE3q+K5iBj+1YxBxkbHxVzkKmx2LQ/JrKa98dUXlWH9A59V0rz/pjIqnQgKzPvr3qdHsrrNR/7E1nQcOzPHOsdHCfWAm8C7o+Dvzbuo8AFEXFtf/m7wC0NslYAX4+IU+lO6bs1M7fN83+aZkXENyNiNd158XuAd1bm9V9rfX7p2L79G/hCRMwA/wVafKbGWNb7CsbHmBvo3np4Dd0K9huqgiJiHXAjsBy4KyLuy8xXFcV9CXgacE//rspfZOa7irIAbuhfNJ+i+40GlVmL6bNFx/SQzcDmiNhF9xbmq7P/UUSRxTw3/e20748xG4v642zg9v54mgG+nZnbI2IH7TtkLKuiQ8ay/kD7DhnL2lrQH4NZDe73iPKK5gVjWafTvkOmHsfWHTK2X4/Svj/GsirnH+8FtvTP05+At9DPfQrmIIdlFc5BhvZrBzVzkKGsmwvnH0N5VYayvlg0BxnKeoyaOcjYY1gxBxnKupO6+cdQ3psrOiS6U7VeydwxUPI9zFBWVX+M7FeZkbyS+ffEvjUb+1H7fYIkSZIkSZJONCfFKWmSJEmSJElqxwUjSZIkSZIkzeGCkSRJkiRJkuZwwUiSJEmSJElzuGAkSZIkSZKkOVwwkiRJOgIRsS4iMiKev9TbIkmSVMUFI0mSpCOzEfgZsGGpN0SSJKmKC0aSJEkLFBHLgLXANfQLRhFxSkR8OSIeiIhtEfH9iFjff+2SiPhpROyMiLsjYsUSbr4kSdKCuWAkSZK0cK8Dtmfm74F/RcQa4PXASuBi4G3ApQARcRpwI7A+My8BNgOfWoqNliRJOlIzS70BkiRJJ5CNwOf7f3+nv3wacFtmPgXsi4gf91+/CFgF3BMRAKcCDy3u5kqSJB0dF4wkSZIWICKeDVwOrIqIpFsASuD2sf8CPJCZly7SJkqSJDXjKWmSJEkLsx74Rmaen5krM/M84M/Aw8BV/WcZnQ1c1t9+N7A8Ig6cohYRL1iKDZckSTpSLhhJkiQtzEYOfzfRVuA5wF5gF/BV4JfAI5n5BN0i02ci4jfAfcBLFm9zJUmSjl5k5lJvgyRJ0gktIpZl5qP9aWv3Amszc99Sb5ckSdLR8jOMJEmSjt22iHgmcDrwSReLJEnSic53GEmSJEmSJGkOP8NIkiRJkiRJc7hgJEmSJEmSpDlcMJIkSZIkSdIcLhhJkiRJkiRpDheMJEmSJEmSNIcLRpIkSZIkSZrj/x0Oj8DEC7c6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,8))\n",
    "sns.countplot('age', hue = 'target', data = df)\n",
    "\n",
    "plt.title('Heart Disease Frequency for Age')\n",
    "plt.legend(['No Disease','Yes Disease'])\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "\n",
    "plt.scatter(df['age'], df['chol'], s = 200)\n",
    "\n",
    "plt.xlabel('Age', fontsize = 20)\n",
    "plt.ylabel('Cholestrol', fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop('target',axis = 1)\n",
    "target = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = features[[ 'sex', 'cp', 'fbs', 'restecg',\n",
    "       'exang', 'slope', 'ca', 'thal']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>exang</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  cp  fbs  restecg  exang  slope  ca  thal\n",
       "0    1   3    1        0      0      0   0     1\n",
       "1    1   2    0        1      0      0   0     2\n",
       "2    0   1    0        0      0      2   0     2\n",
       "3    1   1    0        1      0      2   0     2\n",
       "4    0   0    0        1      1      2   0     2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>150</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>187</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>172</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>178</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>163</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  trestbps  chol  thalach  oldpeak\n",
       "0   63       145   233      150      2.3\n",
       "1   37       130   250      187      3.5\n",
       "2   41       130   204      172      1.4\n",
       "3   56       120   236      178      0.8\n",
       "4   57       120   354      163      0.6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = features[['age','trestbps','chol','thalach','oldpeak']].copy()\n",
    "numeric_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "\n",
    "numeric_features = pd.DataFrame(sc.fit_transform(numeric_features), columns = numeric_features.columns, \n",
    "                                index = numeric_features.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>303.0</td>\n",
       "      <td>5.825923e-17</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>-2.797624</td>\n",
       "      <td>-0.757280</td>\n",
       "      <td>0.069886</td>\n",
       "      <td>0.731619</td>\n",
       "      <td>2.496240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>303.0</td>\n",
       "      <td>-7.146832e-16</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>-2.148802</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>3.905165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>-9.828955e-17</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>-2.324160</td>\n",
       "      <td>-0.681494</td>\n",
       "      <td>-0.121055</td>\n",
       "      <td>0.545674</td>\n",
       "      <td>6.140401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>-5.203025e-16</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>-3.439267</td>\n",
       "      <td>-0.706111</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.715131</td>\n",
       "      <td>2.289429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>-3.140136e-16</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>-0.896862</td>\n",
       "      <td>-0.896862</td>\n",
       "      <td>-0.206705</td>\n",
       "      <td>0.483451</td>\n",
       "      <td>4.451851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count          mean       std       min       25%       50%  \\\n",
       "age       303.0  5.825923e-17  1.001654 -2.797624 -0.757280  0.069886   \n",
       "trestbps  303.0 -7.146832e-16  1.001654 -2.148802 -0.663867 -0.092738   \n",
       "chol      303.0 -9.828955e-17  1.001654 -2.324160 -0.681494 -0.121055   \n",
       "thalach   303.0 -5.203025e-16  1.001654 -3.439267 -0.706111  0.146634   \n",
       "oldpeak   303.0 -3.140136e-16  1.001654 -0.896862 -0.896862 -0.206705   \n",
       "\n",
       "               75%       max  \n",
       "age       0.731619  2.496240  \n",
       "trestbps  0.478391  3.905165  \n",
       "chol      0.545674  6.140401  \n",
       "thalach   0.715131  2.289429  \n",
       "oldpeak   0.483451  4.451851  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>exang</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952197</td>\n",
       "      <td>0.763956</td>\n",
       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "      <td>1.087338</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.915313</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.072199</td>\n",
       "      <td>1.633471</td>\n",
       "      <td>2.122573</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.474158</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>-0.816773</td>\n",
       "      <td>0.977514</td>\n",
       "      <td>0.310912</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.180175</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>-0.198357</td>\n",
       "      <td>1.239897</td>\n",
       "      <td>-0.206705</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>-0.663867</td>\n",
       "      <td>2.082050</td>\n",
       "      <td>0.583939</td>\n",
       "      <td>-0.379244</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  trestbps      chol   thalach   oldpeak  sex  cp  fbs  restecg  \\\n",
       "0  0.952197  0.763956 -0.256334  0.015443  1.087338    1   3    1        0   \n",
       "1 -1.915313 -0.092738  0.072199  1.633471  2.122573    1   2    0        1   \n",
       "2 -1.474158 -0.092738 -0.816773  0.977514  0.310912    0   1    0        0   \n",
       "3  0.180175 -0.663867 -0.198357  1.239897 -0.206705    1   1    0        1   \n",
       "4  0.290464 -0.663867  2.082050  0.583939 -0.379244    0   0    0        1   \n",
       "\n",
       "   exang  slope  ca  thal  \n",
       "0      0      0   0     1  \n",
       "1      0      0   0     2  \n",
       "2      0      2   0     2  \n",
       "3      0      2   0     2  \n",
       "4      1      2   0     2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_features = pd.concat([numeric_features, categorical_features], axis = 1, sort = False)\n",
    "processed_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(processed_features, target, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val,y_train,y_val = train_test_split(x_train,y_train,test_size = 0.15, random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205, 13), (61, 13), (37, 13))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, x_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205,), (61,), (37,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_test.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    \n",
    "    inputs = tf.keras.Input(shape = (x_train.shape[1],))\n",
    "    \n",
    "    dense_layer1 = layers.Dense(12, activation = 'relu')\n",
    "    x = dense_layer1(inputs)\n",
    "    \n",
    "    dropout_layer = layers.Dropout(0.3)\n",
    "    x = dropout_layer(x)\n",
    "    \n",
    "    dense_layer2 = layers.Dense(8, activation = 'relu')\n",
    "    x = dense_layer2(x)\n",
    "    \n",
    "    predictions_layer = layers.Dense(1, activation = 'sigmoid')\n",
    "    predictions = predictions_layer(x)\n",
    "    \n",
    "    model = tf.keras.Model(inputs = inputs, outputs = predictions)\n",
    "    \n",
    "    model.summary()\n",
    "    \n",
    "    model.compile(optimizer = tf.keras.optimizers.Adam(0.001),\n",
    "                 loss = tf.keras.losses.BinaryCrossentropy(),\n",
    "                 metrics = ['accuracy',\n",
    "                 tf.keras.metrics.Precision(0.5),\n",
    "                 tf.keras.metrics.Recall(0.5)])\n",
    "    return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_12 (InputLayer)        [(None, 13)]              0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 12)                168       \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 281\n",
      "Trainable params: 281\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keras.utils.plot_model(model, show_shapes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ShuffleDataset shapes: ((None, 13), (None,)), types: (tf.float64, tf.int64)>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train = tf.data.Dataset.from_tensor_slices((x_train.values,y_train.values))\n",
    "dataset_train = dataset_train.batch(16)\n",
    "\n",
    "dataset_train.shuffle(128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_val = tf.data.Dataset.from_tensor_slices((x_val.values,y_val.values))\n",
    "dataset_val = dataset_val.batch(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_23\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_13 (InputLayer)        [(None, 13)]              0         \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 12)                168       \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 281\n",
      "Trainable params: 281\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "13/13 [==============================] - 0s 25ms/step - loss: 0.7777 - accuracy: 0.5122 - precision_10: 0.5449 - recall_10: 0.7456 - val_loss: 0.7835 - val_accuracy: 0.3514 - val_precision_10: 0.4286 - val_recall_10: 0.6000\n",
      "Epoch 2/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.7946 - accuracy: 0.4732 - precision_10: 0.5200 - recall_10: 0.6842 - val_loss: 0.7450 - val_accuracy: 0.4595 - val_precision_10: 0.5000 - val_recall_10: 0.6500\n",
      "Epoch 3/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.7712 - accuracy: 0.4683 - precision_10: 0.5166 - recall_10: 0.6842 - val_loss: 0.7187 - val_accuracy: 0.5405 - val_precision_10: 0.5556 - val_recall_10: 0.7500\n",
      "Epoch 4/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.7083 - accuracy: 0.5805 - precision_10: 0.5909 - recall_10: 0.7982 - val_loss: 0.6997 - val_accuracy: 0.5676 - val_precision_10: 0.5769 - val_recall_10: 0.7500\n",
      "Epoch 5/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.7118 - accuracy: 0.5463 - precision_10: 0.5686 - recall_10: 0.7632 - val_loss: 0.6859 - val_accuracy: 0.5676 - val_precision_10: 0.5833 - val_recall_10: 0.7000\n",
      "Epoch 6/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6967 - accuracy: 0.5512 - precision_10: 0.5753 - recall_10: 0.7368 - val_loss: 0.6738 - val_accuracy: 0.5946 - val_precision_10: 0.6087 - val_recall_10: 0.7000\n",
      "Epoch 7/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6663 - accuracy: 0.6390 - precision_10: 0.6389 - recall_10: 0.8070 - val_loss: 0.6639 - val_accuracy: 0.6216 - val_precision_10: 0.6250 - val_recall_10: 0.7500\n",
      "Epoch 8/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6508 - accuracy: 0.6390 - precision_10: 0.6429 - recall_10: 0.7895 - val_loss: 0.6538 - val_accuracy: 0.7027 - val_precision_10: 0.6957 - val_recall_10: 0.8000\n",
      "Epoch 9/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6415 - accuracy: 0.7024 - precision_10: 0.6906 - recall_10: 0.8421 - val_loss: 0.6417 - val_accuracy: 0.7027 - val_precision_10: 0.6957 - val_recall_10: 0.8000\n",
      "Epoch 10/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6349 - accuracy: 0.7073 - precision_10: 0.7250 - recall_10: 0.7632 - val_loss: 0.6323 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 11/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.6163 - accuracy: 0.6927 - precision_10: 0.7008 - recall_10: 0.7807 - val_loss: 0.6234 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 12/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.5979 - accuracy: 0.7317 - precision_10: 0.7323 - recall_10: 0.8158 - val_loss: 0.6148 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 13/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5912 - accuracy: 0.7463 - precision_10: 0.7422 - recall_10: 0.8333 - val_loss: 0.6043 - val_accuracy: 0.7027 - val_precision_10: 0.6667 - val_recall_10: 0.9000\n",
      "Epoch 14/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5689 - accuracy: 0.7610 - precision_10: 0.7686 - recall_10: 0.8158 - val_loss: 0.5937 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 15/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5661 - accuracy: 0.7561 - precision_10: 0.7462 - recall_10: 0.8509 - val_loss: 0.5829 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 16/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5610 - accuracy: 0.7610 - precision_10: 0.7686 - recall_10: 0.8158 - val_loss: 0.5738 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 17/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5316 - accuracy: 0.7756 - precision_10: 0.7742 - recall_10: 0.8421 - val_loss: 0.5655 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 18/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5455 - accuracy: 0.7463 - precision_10: 0.7422 - recall_10: 0.8333 - val_loss: 0.5559 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 19/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5454 - accuracy: 0.7366 - precision_10: 0.7308 - recall_10: 0.8333 - val_loss: 0.5483 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 20/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5523 - accuracy: 0.7073 - precision_10: 0.7250 - recall_10: 0.7632 - val_loss: 0.5446 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 21/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5161 - accuracy: 0.7756 - precision_10: 0.7615 - recall_10: 0.8684 - val_loss: 0.5418 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 22/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4989 - accuracy: 0.7610 - precision_10: 0.7642 - recall_10: 0.8246 - val_loss: 0.5379 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 23/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.5163 - accuracy: 0.7659 - precision_10: 0.7750 - recall_10: 0.8158 - val_loss: 0.5337 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 24/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4747 - accuracy: 0.7756 - precision_10: 0.7742 - recall_10: 0.8421 - val_loss: 0.5260 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 25/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4892 - accuracy: 0.7707 - precision_10: 0.7815 - recall_10: 0.8158 - val_loss: 0.5201 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 26/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4857 - accuracy: 0.7951 - precision_10: 0.8273 - recall_10: 0.7982 - val_loss: 0.5171 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 27/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7659 - precision_10: 0.7845 - recall_10: 0.7982 - val_loss: 0.5151 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 28/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4461 - accuracy: 0.7951 - precision_10: 0.8051 - recall_10: 0.8333 - val_loss: 0.5115 - val_accuracy: 0.6757 - val_precision_10: 0.6667 - val_recall_10: 0.8000\n",
      "Epoch 29/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4412 - accuracy: 0.8098 - precision_10: 0.8099 - recall_10: 0.8596 - val_loss: 0.5084 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 30/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4438 - accuracy: 0.8146 - precision_10: 0.8016 - recall_10: 0.8860 - val_loss: 0.5039 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 31/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4391 - accuracy: 0.8098 - precision_10: 0.8151 - recall_10: 0.8509 - val_loss: 0.4994 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 32/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4251 - accuracy: 0.8293 - precision_10: 0.8110 - recall_10: 0.9035 - val_loss: 0.4970 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 33/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4205 - accuracy: 0.8195 - precision_10: 0.8291 - recall_10: 0.8509 - val_loss: 0.4954 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 34/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4439 - accuracy: 0.7805 - precision_10: 0.7899 - recall_10: 0.8246 - val_loss: 0.4924 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 35/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4449 - accuracy: 0.7951 - precision_10: 0.8103 - recall_10: 0.8246 - val_loss: 0.4907 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4401 - accuracy: 0.8244 - precision_10: 0.8305 - recall_10: 0.8596 - val_loss: 0.4907 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 37/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4367 - accuracy: 0.7854 - precision_10: 0.7917 - recall_10: 0.8333 - val_loss: 0.4907 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 38/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4483 - accuracy: 0.8049 - precision_10: 0.8190 - recall_10: 0.8333 - val_loss: 0.4907 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 39/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3981 - accuracy: 0.8341 - precision_10: 0.8571 - recall_10: 0.8421 - val_loss: 0.4916 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 40/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4162 - accuracy: 0.8049 - precision_10: 0.8083 - recall_10: 0.8509 - val_loss: 0.4909 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 41/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4266 - accuracy: 0.8146 - precision_10: 0.7969 - recall_10: 0.8947 - val_loss: 0.4900 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 42/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.4295 - accuracy: 0.8341 - precision_10: 0.8175 - recall_10: 0.9035 - val_loss: 0.4907 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 43/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3915 - accuracy: 0.8537 - precision_10: 0.8500 - recall_10: 0.8947 - val_loss: 0.4911 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 44/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4469 - accuracy: 0.7610 - precision_10: 0.7642 - recall_10: 0.8246 - val_loss: 0.4923 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 45/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4018 - accuracy: 0.8341 - precision_10: 0.8390 - recall_10: 0.8684 - val_loss: 0.4924 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 46/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4072 - accuracy: 0.8439 - precision_10: 0.8417 - recall_10: 0.8860 - val_loss: 0.4910 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 47/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3977 - accuracy: 0.8341 - precision_10: 0.8226 - recall_10: 0.8947 - val_loss: 0.4889 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 48/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4259 - accuracy: 0.8000 - precision_10: 0.8017 - recall_10: 0.8509 - val_loss: 0.4891 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 49/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3764 - accuracy: 0.8488 - precision_10: 0.8430 - recall_10: 0.8947 - val_loss: 0.4890 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 50/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3256 - accuracy: 0.8634 - precision_10: 0.8525 - recall_10: 0.9123 - val_loss: 0.4885 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 51/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3962 - accuracy: 0.8049 - precision_10: 0.8190 - recall_10: 0.8333 - val_loss: 0.4883 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 52/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.4060 - accuracy: 0.8244 - precision_10: 0.8047 - recall_10: 0.9035 - val_loss: 0.4896 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 53/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3885 - accuracy: 0.8195 - precision_10: 0.8291 - recall_10: 0.8509 - val_loss: 0.4902 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 54/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3698 - accuracy: 0.8683 - precision_10: 0.8537 - recall_10: 0.9211 - val_loss: 0.4895 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 55/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3754 - accuracy: 0.8390 - precision_10: 0.8403 - recall_10: 0.8772 - val_loss: 0.4904 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 56/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3982 - accuracy: 0.8098 - precision_10: 0.7820 - recall_10: 0.9123 - val_loss: 0.4907 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 57/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3791 - accuracy: 0.8439 - precision_10: 0.8475 - recall_10: 0.8772 - val_loss: 0.4896 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 58/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3569 - accuracy: 0.8878 - precision_10: 0.8824 - recall_10: 0.9211 - val_loss: 0.4894 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 59/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3674 - accuracy: 0.8390 - precision_10: 0.8347 - recall_10: 0.8860 - val_loss: 0.4894 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 60/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3849 - accuracy: 0.8195 - precision_10: 0.8080 - recall_10: 0.8860 - val_loss: 0.4913 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 61/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3795 - accuracy: 0.8098 - precision_10: 0.7953 - recall_10: 0.8860 - val_loss: 0.4910 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 62/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3779 - accuracy: 0.8293 - precision_10: 0.8319 - recall_10: 0.8684 - val_loss: 0.4908 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 63/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3765 - accuracy: 0.8244 - precision_10: 0.8482 - recall_10: 0.8333 - val_loss: 0.4908 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 64/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3943 - accuracy: 0.8244 - precision_10: 0.8250 - recall_10: 0.8684 - val_loss: 0.4904 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 65/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3523 - accuracy: 0.8537 - precision_10: 0.8387 - recall_10: 0.9123 - val_loss: 0.4902 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 66/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3701 - accuracy: 0.8537 - precision_10: 0.8443 - recall_10: 0.9035 - val_loss: 0.4890 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 67/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3551 - accuracy: 0.8537 - precision_10: 0.8443 - recall_10: 0.9035 - val_loss: 0.4887 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 68/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3265 - accuracy: 0.8829 - precision_10: 0.8879 - recall_10: 0.9035 - val_loss: 0.4887 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 69/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3732 - accuracy: 0.8488 - precision_10: 0.8487 - recall_10: 0.8860 - val_loss: 0.4883 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 70/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3526 - accuracy: 0.8439 - precision_10: 0.8361 - recall_10: 0.8947 - val_loss: 0.4884 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 71/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3352 - accuracy: 0.8585 - precision_10: 0.8571 - recall_10: 0.8947 - val_loss: 0.4897 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 72/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3581 - accuracy: 0.8390 - precision_10: 0.8403 - recall_10: 0.8772 - val_loss: 0.4932 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 73/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3406 - accuracy: 0.8488 - precision_10: 0.8217 - recall_10: 0.9298 - val_loss: 0.4929 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 74/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3521 - accuracy: 0.8537 - precision_10: 0.8443 - recall_10: 0.9035 - val_loss: 0.4926 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 75/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3657 - accuracy: 0.8195 - precision_10: 0.8130 - recall_10: 0.8772 - val_loss: 0.4922 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 76/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3383 - accuracy: 0.8683 - precision_10: 0.8480 - recall_10: 0.9298 - val_loss: 0.4943 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 77/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3419 - accuracy: 0.8732 - precision_10: 0.8667 - recall_10: 0.9123 - val_loss: 0.4973 - val_accuracy: 0.7297 - val_precision_10: 0.7083 - val_recall_10: 0.8500\n",
      "Epoch 78/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3496 - accuracy: 0.8390 - precision_10: 0.8347 - recall_10: 0.8860 - val_loss: 0.4977 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 79/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3727 - accuracy: 0.8439 - precision_10: 0.8361 - recall_10: 0.8947 - val_loss: 0.4961 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 80/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3533 - accuracy: 0.8537 - precision_10: 0.8387 - recall_10: 0.9123 - val_loss: 0.4952 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 81/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3501 - accuracy: 0.8390 - precision_10: 0.8347 - recall_10: 0.8860 - val_loss: 0.4961 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 82/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3372 - accuracy: 0.8293 - precision_10: 0.8264 - recall_10: 0.8772 - val_loss: 0.4973 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 83/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3666 - accuracy: 0.8293 - precision_10: 0.8160 - recall_10: 0.8947 - val_loss: 0.4979 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 84/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3470 - accuracy: 0.8585 - precision_10: 0.8455 - recall_10: 0.9123 - val_loss: 0.5006 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 85/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3524 - accuracy: 0.8488 - precision_10: 0.8430 - recall_10: 0.8947 - val_loss: 0.5033 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 86/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3136 - accuracy: 0.8732 - precision_10: 0.8492 - recall_10: 0.9386 - val_loss: 0.5057 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 87/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3147 - accuracy: 0.8927 - precision_10: 0.8710 - recall_10: 0.9474 - val_loss: 0.5080 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 88/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8732 - precision_10: 0.8548 - recall_10: 0.9298 - val_loss: 0.5087 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 89/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3211 - accuracy: 0.8634 - precision_10: 0.8468 - recall_10: 0.9211 - val_loss: 0.5081 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 90/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3527 - accuracy: 0.8537 - precision_10: 0.8559 - recall_10: 0.8860 - val_loss: 0.5078 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 91/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3041 - accuracy: 0.8780 - precision_10: 0.8678 - recall_10: 0.9211 - val_loss: 0.5081 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 92/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3470 - accuracy: 0.8439 - precision_10: 0.8203 - recall_10: 0.9211 - val_loss: 0.5083 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 93/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3341 - accuracy: 0.8780 - precision_10: 0.8870 - recall_10: 0.8947 - val_loss: 0.5079 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 94/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3153 - accuracy: 0.8780 - precision_10: 0.8739 - recall_10: 0.9123 - val_loss: 0.5095 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n",
      "Epoch 95/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3201 - accuracy: 0.8732 - precision_10: 0.8607 - recall_10: 0.9211 - val_loss: 0.5103 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 96/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.2860 - accuracy: 0.8878 - precision_10: 0.8760 - recall_10: 0.9298 - val_loss: 0.5127 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 97/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8390 - precision_10: 0.8347 - recall_10: 0.8860 - val_loss: 0.5139 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 98/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 0.3298 - accuracy: 0.8829 - precision_10: 0.8689 - recall_10: 0.9298 - val_loss: 0.5161 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 99/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3332 - accuracy: 0.8488 - precision_10: 0.8320 - recall_10: 0.9123 - val_loss: 0.5156 - val_accuracy: 0.7297 - val_precision_10: 0.6923 - val_recall_10: 0.9000\n",
      "Epoch 100/100\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3155 - accuracy: 0.8634 - precision_10: 0.8413 - recall_10: 0.9298 - val_loss: 0.5136 - val_accuracy: 0.7027 - val_precision_10: 0.6800 - val_recall_10: 0.8500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1c03ee43ac0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history = model.fit(dataset_train, epochs = num_epochs, validation_data = dataset_val)\n",
    "training_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'precision_10', 'recall_10', 'val_loss', 'val_accuracy', 'val_precision_10', 'val_recall_10'])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc = training_history.history['accuracy']\n",
    "train_loss = training_history.history['loss']\n",
    "\n",
    "precision = training_history.history['precision_10']\n",
    "recall = training_history.history['recall_10']\n",
    "\n",
    "epochs_range = range(num_epochs)\n",
    "\n",
    "plt.figure(figsize = (14,8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.plot(epochs_range,train_acc, label = \"Training Accuracy\")\n",
    "plt.plot(epochs_range,train_loss, label = \"Training Loss\")\n",
    "\n",
    "plt.title(\"Accuracy and Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.plot(epochs_range, precision, label = \"Precision\")\n",
    "plt.plot(epochs_range, recall, label = \"Recall\")\n",
    "\n",
    "plt.title(\"Precision and Recall\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 998us/step - loss: 0.5576 - accuracy: 0.7213 - precision_10: 0.6842 - recall_10: 0.8387\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "loss            0.557562\n",
       "accuracy        0.721311\n",
       "precision_10    0.684211\n",
       "recall_10       0.838710\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.evaluate(x_test,y_test)\n",
    "\n",
    "score_df = pd.Series(score, index = model.metrics_names)\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00614873],\n",
       "       [0.7852839 ],\n",
       "       [0.04615679],\n",
       "       [0.01945293],\n",
       "       [0.02074376],\n",
       "       [0.01471949],\n",
       "       [0.00518247],\n",
       "       [0.40783927],\n",
       "       [0.00687584],\n",
       "       [0.9614452 ]], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y_pred = model.predict(x_test)\n",
    "\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.where(y_pred >= 0.5, 1, y_pred)\n",
    "y_pred = np.where(y_pred < 0.5, 0, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_results = pd.DataFrame({\"y_test\":y_test.values.flatten(),\n",
    "                            \"y_pred\":y_pred.flatten().astype(\"int32\")}, index = range(len(y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
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       "    y_test  y_pred\n",
       "57       0       0\n",
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       "43       0       0\n",
       "24       1       1\n",
       "50       1       1\n",
       "8        0       0\n",
       "53       1       1\n",
       "3        0       0\n",
       "56       1       1"
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     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pred_results.sample(10)"
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  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
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       "y_test   0   1\n",
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     "execution_count": 83,
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   "execution_count": null,
   "metadata": {},
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